In [2]:

    

from ggplot import *

import pandas as pd
import numpy as np

?ggplot


    

In [3]:

    

# ?diamonds
diamonds.head() # similar for data sets 'meat', 'mtcars', and 'pageviews'


    

    
Out[3]:







  

    

      

      
carat
      
cut
      
color
      
clarity
      
depth
      
table
      
price
      
x
      
y
      
z
    
  
  

    

      
0
      
0.23
      
Ideal
      
E
      
SI2
      
61.5
      
55.0
      
326
      
3.95
      
3.98
      
2.43
    
    

      
1
      
0.21
      
Premium
      
E
      
SI1
      
59.8
      
61.0
      
326
      
3.89
      
3.84
      
2.31
    
    

      
2
      
0.23
      
Good
      
E
      
VS1
      
56.9
      
65.0
      
327
      
4.05
      
4.07
      
2.31
    
    

      
3
      
0.29
      
Premium
      
I
      
VS2
      
62.4
      
58.0
      
334
      
4.20
      
4.23
      
2.63
    
    

      
4
      
0.31
      
Good
      
J
      
SI2
      
63.3
      
58.0
      
335
      
4.34
      
4.35
      
2.75
    
  

In [4]:

    

type(diamonds)


    

    
Out[4]:





pandas.core.frame.DataFrame

In [5]:

    

?ggplot


    

In [6]:

    

p = ggplot(aes(x = 'carat', y = 'price'), data = diamonds)
p


    

    













    
Out[6]:





<ggplot: (-9223371872591522339)>

In [7]:

    

p1 = p + geom_point()  
p1


    

    













    
Out[7]:





<ggplot: (-9223371872591522339)>

In [8]:

    

p2 = p1 + facet_wrap("cut")
p2


    

    













    
Out[8]:





<ggplot: (-9223371872591522339)>

In [9]:

    

p3 = p2 + theme_bw()
p3


    

    













    
Out[9]:





<ggplot: (-9223371872591522339)>